Overview

Dataset statistics

Number of variables12
Number of observations1199
Missing cells0
Missing cells (%)0.0%
Duplicate rows141
Duplicate rows (%)11.8%
Total size in memory112.5 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 141 (11.8%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
pH is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
citric acid has 98 (8.2%) zerosZeros

Reproduction

Analysis started2023-04-17 23:16:47.456916
Analysis finished2023-04-17 23:17:25.576414
Duration38.12 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct91
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3169308
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:25.761677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.19
Q17.1
median7.9
Q39.2
95-th percentile11.6
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7143717
Coefficient of variation (CV)0.20613033
Kurtosis1.1763052
Mean8.3169308
Median Absolute Deviation (MAD)1
Skewness0.96943874
Sum9972
Variance2.9390704
MonotonicityNot monotonic
2023-04-17T19:17:26.182813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1 45
 
3.8%
7.2 45
 
3.8%
7.7 43
 
3.6%
7.8 41
 
3.4%
7.5 41
 
3.4%
7 35
 
2.9%
7.4 34
 
2.8%
7.6 34
 
2.8%
6.9 34
 
2.8%
6.8 33
 
2.8%
Other values (81) 814
67.9%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 4
 
0.3%
5.1 3
 
0.3%
5.2 6
0.5%
5.3 4
 
0.3%
5.4 2
 
0.2%
5.6 11
0.9%
5.7 1
 
0.1%
ValueCountFrequency (%)
15.9 1
 
0.1%
15.6 2
0.2%
15.5 1
 
0.1%
15 1
 
0.1%
13.8 1
 
0.1%
13.7 1
 
0.1%
13.4 1
 
0.1%
13.3 3
0.3%
13.2 2
0.2%
13 3
0.3%

volatile acidity
Real number (ℝ)

Distinct136
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53190158
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:26.716584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.28
Q10.4
median0.52
Q30.64
95-th percentile0.85
Maximum1.58
Range1.46
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.18021716
Coefficient of variation (CV)0.33881674
Kurtosis1.5227019
Mean0.53190158
Median Absolute Deviation (MAD)0.12
Skewness0.76985223
Sum637.75
Variance0.032478226
MonotonicityNot monotonic
2023-04-17T19:17:27.059092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 36
 
3.0%
0.43 34
 
2.8%
0.59 33
 
2.8%
0.6 32
 
2.7%
0.58 29
 
2.4%
0.4 29
 
2.4%
0.41 28
 
2.3%
0.36 27
 
2.3%
0.52 27
 
2.3%
0.38 27
 
2.3%
Other values (126) 897
74.8%
ValueCountFrequency (%)
0.12 1
 
0.1%
0.16 1
 
0.1%
0.18 7
0.6%
0.19 1
 
0.1%
0.2 3
0.3%
0.21 3
0.3%
0.22 4
0.3%
0.23 4
0.3%
0.24 7
0.6%
0.25 6
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.2%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.3%
1.025 1
 
0.1%
1.02 4
0.3%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27224354
Minimum0
Maximum1
Zeros98
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:27.390107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.26
Q30.43
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.1954509
Coefficient of variation (CV)0.7179267
Kurtosis-0.76447307
Mean0.27224354
Median Absolute Deviation (MAD)0.17
Skewness0.32196371
Sum326.42
Variance0.038201056
MonotonicityNot monotonic
2023-04-17T19:17:27.724354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
 
8.2%
0.49 53
 
4.4%
0.24 36
 
3.0%
0.02 35
 
2.9%
0.26 31
 
2.6%
0.01 29
 
2.4%
0.32 28
 
2.3%
0.21 27
 
2.3%
0.1 26
 
2.2%
0.03 24
 
2.0%
Other values (69) 812
67.7%
ValueCountFrequency (%)
0 98
8.2%
0.01 29
 
2.4%
0.02 35
 
2.9%
0.03 24
 
2.0%
0.04 20
 
1.7%
0.05 13
 
1.1%
0.06 16
 
1.3%
0.07 18
 
1.5%
0.08 21
 
1.8%
0.09 23
 
1.9%
ValueCountFrequency (%)
1 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.3%
0.75 1
 
0.1%
0.74 3
0.3%
0.73 3
0.3%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 1
 
0.1%
0.69 2
0.2%

residual sugar
Real number (ℝ)

Distinct80
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.547623
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:27.987725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.6
95-th percentile5.155
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.4118656
Coefficient of variation (CV)0.55418938
Kurtosis27.81632
Mean2.547623
Median Absolute Deviation (MAD)0.3
Skewness4.4683344
Sum3054.6
Variance1.9933645
MonotonicityNot monotonic
2023-04-17T19:17:28.413763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 115
 
9.6%
2.2 104
 
8.7%
2.1 101
 
8.4%
1.8 95
 
7.9%
1.9 83
 
6.9%
2.3 78
 
6.5%
2.4 65
 
5.4%
2.5 64
 
5.3%
2.6 61
 
5.1%
1.7 56
 
4.7%
Other values (70) 377
31.4%
ValueCountFrequency (%)
0.9 2
 
0.2%
1.2 4
 
0.3%
1.3 4
 
0.3%
1.4 26
 
2.2%
1.5 26
 
2.2%
1.6 41
3.4%
1.7 56
4.7%
1.75 2
 
0.2%
1.8 95
7.9%
1.9 83
6.9%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 1
0.1%
13.9 1
0.1%
13.8 2
0.2%
12.9 1
0.1%
11 1
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%
8.8 2
0.2%

chlorides
Real number (ℝ)

Distinct140
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08846372
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:28.639932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.07
median0.08
Q30.091
95-th percentile0.132
Maximum0.611
Range0.599
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.049356224
Coefficient of variation (CV)0.55792617
Kurtosis39.423002
Mean0.08846372
Median Absolute Deviation (MAD)0.01
Skewness5.5496006
Sum106.068
Variance0.0024360369
MonotonicityNot monotonic
2023-04-17T19:17:28.931708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 55
 
4.6%
0.074 38
 
3.2%
0.076 37
 
3.1%
0.077 36
 
3.0%
0.075 35
 
2.9%
0.084 35
 
2.9%
0.079 35
 
2.9%
0.082 34
 
2.8%
0.078 33
 
2.8%
0.071 33
 
2.8%
Other values (130) 828
69.1%
ValueCountFrequency (%)
0.012 2
0.2%
0.034 1
 
0.1%
0.038 2
0.2%
0.039 3
0.3%
0.041 2
0.2%
0.042 2
0.2%
0.043 1
 
0.1%
0.044 1
 
0.1%
0.045 4
0.3%
0.046 3
0.3%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.415 3
0.3%
0.414 2
0.2%
0.403 1
 
0.1%
0.401 1
 
0.1%
0.387 1
 
0.1%
0.369 1
 
0.1%
0.368 1
 
0.1%

free sulfur dioxide
Real number (ℝ)

Distinct55
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.864887
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:29.204696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum68
Range67
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.254101
Coefficient of variation (CV)0.64633935
Kurtosis1.8082678
Mean15.864887
Median Absolute Deviation (MAD)7
Skewness1.1854548
Sum19022
Variance105.14659
MonotonicityNot monotonic
2023-04-17T19:17:29.647133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 105
 
8.8%
5 80
 
6.7%
15 60
 
5.0%
10 60
 
5.0%
12 59
 
4.9%
7 58
 
4.8%
17 51
 
4.3%
9 46
 
3.8%
16 44
 
3.7%
13 42
 
3.5%
Other values (45) 594
49.5%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 38
 
3.2%
4 25
 
2.1%
5 80
6.7%
5.5 1
 
0.1%
6 105
8.8%
7 58
4.8%
8 36
 
3.0%
9 46
3.8%
ValueCountFrequency (%)
68 2
0.2%
66 1
 
0.1%
57 1
 
0.1%
55 1
 
0.1%
53 1
 
0.1%
52 1
 
0.1%
51 1
 
0.1%
50 2
0.2%
48 3
0.3%
45 2
0.2%

total sulfur dioxide
Real number (ℝ)

Distinct138
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.766055
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:30.116264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q363
95-th percentile114.1
Maximum289
Range283
Interquartile range (IQR)41

Descriptive statistics

Standard deviation33.026392
Coefficient of variation (CV)0.70620437
Kurtosis2.8249473
Mean46.766055
Median Absolute Deviation (MAD)18
Skewness1.3895075
Sum56072.5
Variance1090.7426
MonotonicityNot monotonic
2023-04-17T19:17:30.637634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 33
 
2.8%
14 28
 
2.3%
18 28
 
2.3%
20 28
 
2.3%
23 27
 
2.3%
24 26
 
2.2%
38 26
 
2.2%
10 23
 
1.9%
16 23
 
1.9%
19 23
 
1.9%
Other values (128) 934
77.9%
ValueCountFrequency (%)
6 3
 
0.3%
7 3
 
0.3%
8 11
 
0.9%
9 12
1.0%
10 23
1.9%
11 16
1.3%
12 18
1.5%
13 20
1.7%
14 28
2.3%
15 22
1.8%
ValueCountFrequency (%)
289 1
 
0.1%
155 1
 
0.1%
152 1
 
0.1%
151 2
0.2%
149 1
 
0.1%
147 2
0.2%
145 3
0.3%
144 3
0.3%
143 2
0.2%
142 1
 
0.1%

density
Real number (ℝ)

Distinct369
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99676455
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:31.099424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.9937
Q10.99566
median0.9968
Q30.99783
95-th percentile0.9998
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.00217

Descriptive statistics

Standard deviation0.001842276
Coefficient of variation (CV)0.0018482559
Kurtosis1.0918827
Mean0.99676455
Median Absolute Deviation (MAD)0.0011
Skewness0.03299399
Sum1195.1207
Variance3.3939807 × 10-6
MonotonicityNot monotonic
2023-04-17T19:17:31.566074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9968 29
 
2.4%
0.9976 26
 
2.2%
0.9972 26
 
2.2%
0.9978 26
 
2.2%
0.998 20
 
1.7%
0.997 19
 
1.6%
0.9962 18
 
1.5%
0.9964 18
 
1.5%
0.9994 18
 
1.5%
0.9982 17
 
1.4%
Other values (359) 982
81.9%
ValueCountFrequency (%)
0.99007 2
0.2%
0.9902 1
0.1%
0.99064 2
0.2%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.2%
0.99182 2
0.2%
0.9921 1
0.1%
0.9922 2
0.2%
ValueCountFrequency (%)
1.00369 1
0.1%
1.0032 1
0.1%
1.00315 2
0.2%
1.00289 1
0.1%
1.0026 2
0.2%
1.00242 2
0.2%
1.0022 1
0.1%
1.0021 2
0.2%
1.0015 1
0.1%
1.0014 2
0.2%

pH
Real number (ℝ)

Distinct85
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3112594
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:31.736340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.07
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15425625
Coefficient of variation (CV)0.046585372
Kurtosis0.72647205
Mean3.3112594
Median Absolute Deviation (MAD)0.1
Skewness0.19146419
Sum3970.2
Variance0.02379499
MonotonicityNot monotonic
2023-04-17T19:17:32.084118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 49
 
4.1%
3.26 44
 
3.7%
3.36 40
 
3.3%
3.38 38
 
3.2%
3.39 35
 
2.9%
3.29 35
 
2.9%
3.32 32
 
2.7%
3.16 31
 
2.6%
3.33 31
 
2.6%
3.31 30
 
2.5%
Other values (75) 834
69.6%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.88 2
0.2%
2.89 3
0.3%
2.9 1
 
0.1%
2.92 3
0.3%
2.93 3
0.3%
2.94 3
0.3%
2.95 1
 
0.1%
2.98 3
0.3%
2.99 2
0.2%
ValueCountFrequency (%)
4.01 1
 
0.1%
3.9 2
 
0.2%
3.85 1
 
0.1%
3.78 1
 
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.71 4
0.3%
3.69 4
0.3%
3.68 5
0.4%
3.67 1
 
0.1%

sulphates
Real number (ℝ)

Distinct92
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66001668
Minimum0.37
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:32.573766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.93
Maximum2
Range1.63
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1752313
Coefficient of variation (CV)0.26549527
Kurtosis13.02653
Mean0.66001668
Median Absolute Deviation (MAD)0.08
Skewness2.6364031
Sum791.36
Variance0.03070601
MonotonicityNot monotonic
2023-04-17T19:17:33.040835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58 51
 
4.3%
0.6 49
 
4.1%
0.62 49
 
4.1%
0.54 47
 
3.9%
0.57 43
 
3.6%
0.56 42
 
3.5%
0.55 41
 
3.4%
0.64 38
 
3.2%
0.53 37
 
3.1%
0.61 35
 
2.9%
Other values (82) 767
64.0%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.39 3
 
0.3%
0.4 3
 
0.3%
0.42 4
 
0.3%
0.43 7
 
0.6%
0.44 13
1.1%
0.45 6
 
0.5%
0.46 16
1.3%
0.47 14
1.2%
0.48 26
2.2%
ValueCountFrequency (%)
2 1
0.1%
1.98 1
0.1%
1.95 2
0.2%
1.62 1
0.1%
1.61 1
0.1%
1.59 1
0.1%
1.56 1
0.1%
1.36 1
0.1%
1.34 1
0.1%
1.33 1
0.1%

alcohol
Real number (ℝ)

Distinct60
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.420183
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:33.396589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0590981
Coefficient of variation (CV)0.10163911
Kurtosis0.2891754
Mean10.420183
Median Absolute Deviation (MAD)0.7
Skewness0.88851264
Sum12493.8
Variance1.1216888
MonotonicityNot monotonic
2023-04-17T19:17:33.837823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 106
 
8.8%
9.4 76
 
6.3%
9.8 59
 
4.9%
10.5 56
 
4.7%
9.2 53
 
4.4%
10 49
 
4.1%
9.6 47
 
3.9%
11 46
 
3.8%
9.3 45
 
3.8%
9.7 41
 
3.4%
Other values (50) 621
51.8%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.8 2
 
0.2%
9 21
 
1.8%
9.05 1
 
0.1%
9.1 19
 
1.6%
9.2 53
4.4%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
9.3 45
3.8%
9.4 76
6.3%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 4
0.3%
13.6 4
0.3%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.3%
13.3 3
0.3%
13.1 1
 
0.1%
13 3
0.3%
12.9 6
0.5%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6271893
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-04-17T19:17:34.248702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81428893
Coefficient of variation (CV)0.14470616
Kurtosis0.32526547
Mean5.6271893
Median Absolute Deviation (MAD)1
Skewness0.19443628
Sum6747
Variance0.66306647
MonotonicityNot monotonic
2023-04-17T19:17:34.515539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 517
43.1%
6 469
39.1%
7 151
 
12.6%
4 40
 
3.3%
8 13
 
1.1%
3 9
 
0.8%
ValueCountFrequency (%)
3 9
 
0.8%
4 40
 
3.3%
5 517
43.1%
6 469
39.1%
7 151
 
12.6%
8 13
 
1.1%
ValueCountFrequency (%)
8 13
 
1.1%
7 151
 
12.6%
6 469
39.1%
5 517
43.1%
4 40
 
3.3%
3 9
 
0.8%

Interactions

2023-04-17T19:17:20.477867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.091007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:50.102828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:53.139376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:55.591344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.935898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:00.624008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:03.457665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:06.109404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:09.300144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:12.861628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:16.461067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:20.736006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.250132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:50.229853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:53.337891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:55.897179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:58.343112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:00.756312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:03.914437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:06.308959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:09.747418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:13.069644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:16.992697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:21.115724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.369378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:50.422499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:53.450932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:56.069847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:58.555664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:01.049022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:04.194219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:06.587948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:09.953953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:13.232683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:17.344485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:21.460837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.515045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:50.582620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:53.617445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:56.176549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:58.661984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:01.413882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:04.321768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:07.025620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:10.355488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:13.353715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:17.621753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:21.847666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.656796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:50.923791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:53.908137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:56.371659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:58.776257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:01.643870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:04.508238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:07.378079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:10.517412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:13.569483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:17.866193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:22.115741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:48.841864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:51.285248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.182743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:56.495785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:58.904647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:01.781644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:04.709985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:07.600764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:10.855628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:13.862897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:18.149173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:22.277017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.059662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:51.653775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.307227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:56.829836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:59.071967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:02.134873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:04.909377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:07.898702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:11.186457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:14.176208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:18.498969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:22.622063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.198203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:51.969045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.526062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.087981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:59.219655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:02.421772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:05.071947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:08.085136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:11.524807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:14.494785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:18.709666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:23.046071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.395681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:52.218665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.632834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.308849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:59.608179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:02.559475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:05.291771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:08.344682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:11.777294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:14.967499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:19.071625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:23.365727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.573681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:52.421682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.811697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.427049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:59.872318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:02.739664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:05.409961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:08.468239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:12.025468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:15.377968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:19.514864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:23.807469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.822086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:52.572082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:54.989666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.589456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:00.242444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:02.923726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:05.599781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:08.685857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:12.372982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:15.802647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:19.718632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:24.100896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:49.981879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:52.698107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:55.207844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:57.831307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:00.383736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:03.099676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:05.862740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:09.025507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:12.678733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:16.141140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:17:20.138707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-17T19:17:34.846093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.2640.6560.2050.241-0.177-0.0920.607-0.7000.197-0.0520.090
volatile acidity-0.2641.000-0.6040.0270.1740.0360.1060.0220.218-0.341-0.233-0.366
citric acid0.656-0.6041.0000.1800.122-0.0890.0070.356-0.5430.3290.0950.190
residual sugar0.2050.0270.1801.0000.1860.0480.1110.426-0.0630.0390.1420.041
chlorides0.2410.1740.1220.1861.000-0.0030.1200.387-0.237-0.013-0.271-0.196
free sulfur dioxide-0.1770.036-0.0890.048-0.0031.0000.797-0.0290.1180.047-0.100-0.072
total sulfur dioxide-0.0920.1060.0070.1110.1200.7971.0000.131-0.014-0.008-0.277-0.220
density0.6070.0220.3560.4260.387-0.0290.1311.000-0.2950.146-0.436-0.189
pH-0.7000.218-0.543-0.063-0.2370.118-0.014-0.2951.000-0.0690.183-0.014
sulphates0.197-0.3410.3290.039-0.0130.047-0.0080.146-0.0691.0000.2340.382
alcohol-0.052-0.2330.0950.142-0.271-0.100-0.277-0.4360.1830.2341.0000.497
quality0.090-0.3660.1900.041-0.196-0.072-0.220-0.189-0.0140.3820.4971.000

Missing values

2023-04-17T19:17:24.503102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T19:17:25.324870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
011.70.4900.492.20.0835.015.01.000003.190.439.25
18.80.6000.292.20.0985.015.00.998803.360.499.15
27.10.5900.002.10.0919.014.00.994883.420.5511.57
38.30.5400.243.40.07616.0112.00.997603.270.619.45
49.30.7750.272.80.07824.056.00.998403.310.6710.66
56.40.5700.122.30.12025.036.00.995193.470.7111.37
69.90.4000.536.70.0976.019.00.998603.270.8211.77
710.60.4800.642.20.1116.020.00.997003.260.6611.76
88.60.4900.281.90.11020.0136.00.997202.931.959.96
98.60.6350.681.80.40319.056.00.996323.021.159.35
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
11898.10.7300.002.50.08112.024.00.997983.380.469.64
119010.30.5000.422.00.06921.051.00.998203.160.7211.56
11918.80.5500.042.20.11914.056.00.996203.210.6010.96
11926.40.3900.333.30.04612.053.00.992943.360.6212.26
11939.40.4000.472.50.0876.020.00.997723.150.5010.55
11949.10.6000.001.90.0585.010.00.997703.180.6310.46
11958.20.6350.102.10.07325.060.00.996383.290.7510.96
11967.20.6200.062.70.07715.085.00.997463.510.549.55
11977.90.2000.351.70.0547.015.00.994583.320.8011.97
11985.80.2900.261.70.0633.011.00.991503.390.5413.56

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
136.70.4600.241.70.07718.034.00.994803.390.6010.663
247.00.6900.072.50.09115.021.00.995723.380.6011.363
397.20.6950.132.00.07612.020.00.995463.290.5410.153
527.50.5100.021.70.08413.031.00.995383.360.5410.563
908.30.6500.102.90.08917.040.00.998033.290.559.553
1119.30.3600.391.50.08041.055.00.996523.470.7310.963
1209.90.5400.452.30.07116.040.00.999103.390.629.453
05.20.3400.001.80.05027.063.00.991603.680.7914.062
15.60.5000.092.30.04917.099.00.993703.630.6313.052
25.60.6600.002.20.0873.011.00.993783.710.6312.872